Operations | Monitoring | ITSM | DevOps | Cloud

The cloud, as we know it today, was created as recently as 2006. For most of its lifespan since then, companies have been throwing money at cloud services with abandon. The competitive edge gained by having the newest, best, and most powerful tools at their disposal made it worthwhile for companies to spend ever-increasing amounts without too much worry.

While writing our 2024 recap, we found that teams handled over 2.2 million new incidents. Critical incidents alone tripled, increasing from 3,000 in 2023 to 9,200 in 2024. Dealing with such a large volume of incidents is not an easy task. And dealing with them manually is definitely not easy. Your valuable time goes into routine tasks like creating tickets, setting up war rooms, and notifying stakeholders. These keep you from fixing the actual problem.

Large enterprises today grapple with increasingly complex IT environments - spanning multiple cloud services, hybrid infrastructures and countless applications. Exacerbated by technology silos, the sheer volumes of data generated in such environments can quickly overwhelm IT teams, impairing their ability to identify and respond to customer impacting issues before outages strike.

The past few years have seen the incredible rise of cloud-native AI start-ups, many of them born during the pandemic. These companies emerged agile, experimental, and ready to scale. But as their ambitions grow and their AI models become more complex, they face a critical crossroads: how to manage infrastructure sustainably while continuing to innovate at speed. In the early days, public cloud services were the obvious choice.

A disaster recovery plan (DRP) is the strategic backbone of your organization’s resilience. It defines your objectives, outlines responsibilities, and sets the critical promise you make to the business: your recovery time objective (RTO). This plan is indispensable. However, a strategy is worthless without the tactical ability to implement it.

Agentic AI has sparked a ton of excitement in IT. These intelligent agents can analyze signals, interpret requests, and recommend actions with surprising accuracy. But left on their own, they struggle to translate those insights into reliable execution. The end result is a fragmented picture of great thinking... but limited doing. This is why orchestration matters.

Since launching our AI investigations and insights in a research preview, one thing has become clear: automated root cause analysis delivers a significant return on investment. Teams have confirmed that instant insights don’t just save a few minutes; they fundamentally shorten incident response cycles, free up valuable engineering hours, and reduce the business impact of downtime.

One of the biggest challenges in frontend development is identifying when and why users encounter performance issues, whether it’s slow page loads, JavaScript errors, or failed HTTP requests. With Grafana Cloud Frontend Observability — a hosted service for real user monitoring (RUM) — you get immediate, clear, and actionable insights into the end-user experience of your web applications.

Redgate Monitor now supports Oracle Data Guard environments, giving DBAs instant visibility into replication health, lag and role transitions, so Standbys stay in sync and are ready to protect availability and data when needed. DBAs running Oracle Data Guard know that keeping replicas healthy requires constant vigilance. It often involves querying dynamic performance views, such as V$DATAGUARD_STATS, or running Data Guard Broker commands to check lag and role status.

Microsoft Azure Data Factory is a cloud-based data integration service provided by Microsoft Azure. It enables you to create, manage, and automate data workflows that move and transform data from different sources to various destinations. Essentially, ADF allows you to design, orchestrate, and manage data pipelines, making it easier to work with large volumes of data across on-premises and cloud environments.